Energy storage battery state of charge determination method and energy storage system

Abstract

The application embodiment provides a kind of energy storage battery charge state determination method and energy storage system, obtain the initial open circuit voltage of the energy storage battery to be measured;The initial open circuit voltage is corrected based on the aging degree and temperature of the energy storage battery to be measured, to obtain target open circuit voltage;Get voltage power mapping table, voltage power mapping table is based on the multi-order differential characteristics between the open circuit voltage of the energy storage battery to be measured and the power value is constructed, and the mapping relationship between the plurality of voltage feature points and power feature points in voltage power mapping table;The target power value corresponding to target open circuit voltage is determined based on the positional relationship between target open circuit voltage and voltage feature point.The initial open circuit voltage is corrected by combining aging degree and temperature, and the target power value is determined by relying on the voltage power mapping table constructed based on multi-order differential characteristics, effectively eliminates the interference of aging, temperature on open circuit voltage, improves the accuracy of energy storage battery power value estimation.

Description

Technical Field

[0001] This application relates to the field of energy storage control, specifically to a method for determining the state of charge of an energy storage battery and an energy storage system. Background Technology

[0002] In the field of lithium-ion battery state monitoring, traditional battery state of charge (SOC) estimation methods have many limitations. For example, batteries need to be left to stand for a long time (>2 hours) before open circuit voltage (OCV) calibration can be performed, which cannot meet the real-time online application requirements of scenarios such as energy storage systems; battery aging can cause OCV-SOC curve drift, leading to estimation errors; the voltage change in the charging plateau region is gradual, and the resolution of a single differential path is insufficient, making it prone to estimation divergence; at the same time, voltage hysteresis under dynamic operating conditions can cause SOC jumps, feature points are often lost at low temperatures (-20℃), and environmental voltage sampling noise can also cause distortion of the differential signal. These problems all restrict the accuracy and reliability of battery state monitoring. Summary of the Invention

[0003] In view of this, this application provides a method for determining the state of charge of an energy storage battery and an energy storage system, in order to solve the problem of low accuracy in battery SOC estimation in the prior art.

[0004] In a first aspect, embodiments of this application provide a method for determining the state of charge of an energy storage battery, including:

[0005] Obtain the initial open-circuit voltage of the energy storage battery under test;

[0006] The initial open-circuit voltage is corrected based on the aging degree and temperature of the energy storage battery under test to obtain the target open-circuit voltage;

[0007] A voltage-to-capacity mapping table is obtained. The voltage-to-capacity mapping table is constructed based on the multi-order differential characteristics between the open-circuit voltage and the capacity value of the energy storage battery under test. The voltage-to-capacity mapping table contains the mapping relationship between multiple voltage feature points and capacity feature points.

[0008] The target charge value corresponding to the target open circuit voltage is determined based on the positional relationship between the target open circuit voltage and the voltage feature point.

[0009] In one optional embodiment, the step of correcting the initial open-circuit voltage based on the aging degree and temperature of the energy storage battery under test to obtain the target open-circuit voltage includes:

[0010] Obtain the aging compensation coefficient and temperature compensation coefficient corresponding to the material properties of the energy storage battery under test, and obtain the aging ratio value and battery temperature of the energy storage battery under test.

[0011] The first voltage compensation value is determined based on the aging ratio value and the aging compensation coefficient.

[0012] The second voltage compensation value is determined based on the battery temperature, the temperature compensation coefficient, and the first voltage compensation value.

[0013] The target open-circuit voltage is determined based on the second voltage compensation value and the initial open-circuit voltage.

[0014] In an optional embodiment, before obtaining the voltage-to-electricity mapping table, the method further includes:

[0015] In response to the charging test of the energy storage battery under test, the open circuit voltage data and capacity data of the energy storage battery under test are acquired during the charging test.

[0016] The phase transition plateau region is determined based on the first-order differential characteristics of the open-circuit voltage data and the electrical quantity value data;

[0017] Within the phase transition platform region, multiple voltage feature points and corresponding electrical feature points are determined based on the second-order differential characteristics of the open-circuit voltage data and the electrical quantity value data.

[0018] In one optional embodiment, determining the phase transition plateau region based on the first-order differential characteristics of the open-circuit voltage data and the electrical quantity data includes:

[0019] During the charging test, the first derivative of the ratio of the open-circuit voltage data to the power value data is calculated in real time.

[0020] When the value of the first derivative first falls below the second threshold, it is marked as the starting point of the phase transition plateau region. When the value of the first derivative rises back above the second threshold, it is marked as the ending point of the phase transition plateau region.

[0021] In one optional embodiment, determining multiple voltage feature points and the corresponding electrical feature point for each voltage feature point based on the second-order differential features of the open-circuit voltage data and the electrical quantity value data includes:

[0022] Calculate the second derivative of the ratio of the open-circuit voltage data to the electrical quantity data, and generate the corresponding second derivative curve, where the horizontal axis of the second derivative curve is the electrical quantity value and the vertical axis is the second derivative value.

[0023] The voltage characteristic point and the electrical characteristic point are determined based on the negative abrupt change point of the second derivative curve.

[0024] In one optional embodiment, determining the voltage characteristic point and the charge characteristic point based on the negative abrupt change point of the second derivative curve includes:

[0025] Scanning the second derivative curve, when it is detected that the second derivative values ​​of N consecutive sampling points are all lower than the third threshold, the open-circuit voltage and charge value corresponding to the sampling point with the smallest second derivative value are respectively determined as a set of corresponding voltage feature points and charge feature points.

[0026] In an optional embodiment, the method further includes:

[0027] The identified voltage characteristic points are sorted in ascending order of their second derivative values, and the first three voltage characteristic points after sorting are successively labeled as the main peak characteristic point, the secondary peak characteristic point, and the tertiary peak characteristic point.

[0028] In an optional embodiment, before determining the phase transition plateau region based on the first-order differential characteristics of the open-circuit voltage data and the electrical quantity data, the method further includes:

[0029] The open-circuit voltage data is filtered based on the current fluctuation characteristics of the energy storage battery under test.

[0030] In one optional embodiment, filtering the open-circuit voltage data based on the current fluctuation characteristics of the energy storage battery under test includes:

[0031] Calculate the current variance of the energy storage battery under test;

[0032] In response to the current variance value being less than a first threshold, the voltage data is filtered based on a first window value; in response to the current variance value being not less than the first threshold, the voltage data is filtered based on a second window value.

[0033] Wherein, the first window value is smaller than the second window value.

[0034] In one optional embodiment, determining the target charge value corresponding to the target open-circuit voltage based on the positional relationship between the target open-circuit voltage and the voltage feature point includes:

[0035] Calculate the voltage difference between any two consecutive voltage characteristic points, and calculate the energy difference between the corresponding two consecutive energy characteristic points;

[0036] Based on the voltage difference and the charge difference, determine the mapping relationship between open-circuit voltage and charge value between any two consecutive voltage characteristic points;

[0037] Two target voltage feature points are identified where the target open-circuit voltage is located, and the target electrical quantity value corresponding to the target open-circuit voltage is determined based on the target mapping relationship between the open-circuit voltage and the electrical quantity value between the two target voltage feature points.

[0038] In an optional embodiment, the method further includes:

[0039] Temperature correction and aging correction are performed on the target charge value based on the positional relationship between the target open-circuit voltage and the voltage characteristic point.

[0040] Secondly, embodiments of this application provide a device for determining the state of charge of an energy storage battery, comprising:

[0041] Energy storage systems include: electronic devices and computer-readable storage media;

[0042] The electronic device is configured to store a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device performs the method described in any one of the first aspects.

[0043] The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method described in any one of the first aspects.

[0044] The scheme provided in this application obtains the initial open-circuit voltage of the energy storage battery under test; corrects the initial open-circuit voltage based on the aging degree and temperature of the energy storage battery under test to obtain the target open-circuit voltage; obtains a voltage-to-capacity mapping table, which is constructed based on the multi-order differential characteristics between the open-circuit voltage and the capacity value of the energy storage battery under test, and contains the mapping relationship between multiple voltage feature points and capacity feature points; and determines the target capacity value corresponding to the target open-circuit voltage based on the positional relationship between the target open-circuit voltage and the voltage feature points. By combining the aging degree and temperature to correct the initial open-circuit voltage, and relying on the voltage-to-capacity mapping table constructed based on multi-order differential characteristics to determine the target capacity value, the interference of aging and temperature on the open-circuit voltage is effectively eliminated, and the accuracy of energy storage battery capacity estimation is improved. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1A flowchart illustrating a method for determining the charge state of an energy storage battery, provided in an embodiment of this application;

[0047] Figure 2 A flowchart illustrating another method for determining the state of charge of an energy storage battery provided in an embodiment of this application;

[0048] Figure 3 This is an example schematic diagram of a method for determining the charge state of an energy storage battery provided in an embodiment of this application;

[0049] Figure 4 A flowchart illustrating another method for determining the state of charge of an energy storage battery provided in an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0051] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0052] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0053] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0054] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0055] Figure 1 This is a flowchart illustrating a method for determining the state of charge of an energy storage battery, provided as an embodiment of this application. This method can be applied to energy storage systems. Figure 1 As shown, the method may include:

[0056] Step 101: Obtain the initial open-circuit voltage of the energy storage battery under test.

[0057] There is a mapping relationship between the open-circuit voltage and the charge value of the energy storage battery under test. By detecting the open-circuit voltage and performing corresponding calculations, the charge value of the energy storage battery under test can be obtained. The initial open-circuit voltage can be regarded as the raw voltage data obtained by the sensor.

[0058] Step 102: Correct the initial open-circuit voltage based on the aging degree and temperature of the energy storage battery under test to obtain the target open-circuit voltage.

[0059] Temperature variations and aging of the tested energy storage battery can significantly impact open-circuit voltage measurements. Temperature alters the internal chemical reaction state (e.g., ion movement rate, electrode equilibrium), resulting in different initial open-circuit voltages measured at different temperatures for the same charge. Battery aging leads to material loss and deterioration of the interface (e.g., reduced active material, increased byproducts), altering the voltage difference between the positive and negative electrodes. For the same charge, an older battery may have a slightly higher or lower open-circuit voltage than a newer battery, and the curve will be steeper, leading to inaccurate measurements.

[0060] To address this issue, the energy storage system can correct the initial open-circuit voltage based on the aging degree and temperature change of the battery under test, thus obtaining the corrected target open-circuit voltage. Optionally, the energy storage system can obtain the aging compensation coefficient and temperature compensation coefficient corresponding to the material properties of the battery under test, as well as the aging ratio and battery temperature. Then, the energy storage system first determines a first voltage compensation value based on the aging ratio and aging compensation coefficient, and then determines a second voltage compensation value based on the battery temperature, temperature compensation coefficient, and the first voltage compensation value. Finally, the target open-circuit voltage can be determined based on the second voltage compensation value and the initial open-circuit voltage.

[0061] The compensation formula for battery aging may include:

[0062] is the first voltage compensation value, a and b are the aging compensation coefficients, and SOH is the aging ratio value.

[0063] Battery temperature compensation formulas may include: Here, k is the second voltage compensation value, k is the temperature compensation coefficient, and T is the battery temperature. Standard temperature value.

[0064] Finally, based on the formula The target open-circuit voltage can be obtained by adding the second temperature compensation value and the initial open-circuit voltage V_raw. .

[0065] The aforementioned coefficients are determined by the material properties of the energy storage battery. For example, the coefficients for ternary lithium batteries can be taken from the following ranges: a (0.10, 0.16), b (-0.10, -0.06), k (0.03, 0.07), and the coefficients for lithium iron phosphate batteries can be taken from the following ranges: a (0.15, 0.22), b (-0.07, -0.03), k (0.02, 0.04). The value ranges are indicated by parentheses, and the values ​​can be greater than or equal to the first value in the parentheses and less than or equal to the second value in the parentheses. In an optional embodiment, a, b, and k for ternary lithium batteries can be set to 0.12, -0.08, and 0.05, respectively, while a, b, and k for lithium iron phosphate batteries can be set to 0.18, -0.05, and 0.03, respectively.

[0066] In this embodiment, the nonlinear interference of temperature change and battery aging on open circuit voltage measurement is accurately eliminated through the above two-level compensation mechanism. The target open circuit voltage obtained after compensation can truly reflect the open circuit voltage characteristics of the battery under the current aging state and temperature, providing an accurate benchmark for subsequent energy storage capacity (SOC) estimation based on OCV, significantly improving the accuracy of energy storage system state monitoring, and enhancing the reliability and stability of battery management.

[0067] Step 103: Obtain the voltage-capacity mapping table. The voltage-capacity mapping table is constructed based on the multi-order differential characteristics between the open-circuit voltage and the capacity value of the energy storage battery under test. The voltage-capacity mapping table contains the mapping relationship between multiple voltage feature points and capacity feature points.

[0068] During the charging process of an energy storage battery, the change between open-circuit voltage and charge value is not uniform; it passes through a phase transition plateau region. Within this region, the open-circuit voltage changes slowly with the charge value, and the curve slope is low. Several voltage characteristic points exist within this phase transition plateau region, and the corresponding charge values ​​are the charge characteristic points. The correspondence between these voltage and charge characteristic points is relatively stable and less affected by battery temperature and aging. Based on this stable relationship, the target charge value of the energy storage battery can be calculated relatively accurately.

[0069] Optionally, common voltage characteristic points may include the main peak characteristic point, the secondary peak characteristic point, and the tertiary peak characteristic point. The quantity characteristic point corresponding to the main peak characteristic point is 50%, the quantity characteristic point corresponding to the secondary peak characteristic point is 75%, and the quantity characteristic point corresponding to the tertiary peak characteristic point is 90%.

[0070] Step 104: Determine the target charge value corresponding to the target open circuit voltage based on the positional relationship between the target open circuit voltage and the voltage feature point.

[0071] Optionally, the steps for the energy storage system to calculate the target energy value may include: calculating the voltage difference between any two consecutive voltage characteristic points, and calculating the energy difference between the corresponding two consecutive energy characteristic points; determining the mapping relationship between open-circuit voltage and energy value between any two consecutive voltage characteristic points based on the voltage difference and energy difference; determining the two target voltage characteristic points where the target open-circuit voltage is located, and determining the target energy value corresponding to the target open-circuit voltage based on the target mapping relationship between open-circuit voltage and energy value between the two target voltage characteristic points.

[0072] Specifically, when the target open-circuit voltage is located between the main peak characteristic point and the secondary peak characteristic point, the above steps can be summarized by the following formula:

[0073] Where Vmain is the primary peak characteristic point, Vsub is the secondary peak characteristic point, SOCmain is the electrical quantity characteristic point corresponding to the primary peak characteristic point, and SOCsub is the electrical quantity characteristic point corresponding to the secondary peak characteristic point. Typically, the values ​​of Vmain, Vsub, SOCmain, and SOCsub are relatively stable, and can be calculated using the positional relationship between the target open-circuit voltage and Vmain and Vsub. On the other hand It can also be calculated from the positional relationship between the target energy value and SOCmain and SOCsub. The target energy value can be calculated in reverse using λ.

[0074] The following numerical examples illustrate the calculation process of SOCbase, using typical characteristics of lithium iron phosphate batteries as an example, with the following parameter settings:

[0075] Main peak characteristic point (Vmain): 3.2V, corresponding charge of the main peak (SOCmain): 50%; Secondary peak characteristic point (Vsub): 3.3V, corresponding charge of the secondary peak (SOCsub): 75%; Target open-circuit voltage (Vfinal): 3.24V, located between Vmain and Vsub. According to the formula... Substituting the above values, ,Should It can indicate that the target voltage Vfinal is located at 40% of the position between Vmain and Vsub.

[0076] According to the formula Substituting the above values,

[0077] SOCbase = 50% + 0.4×(75% - 50%) = 50% + 0.4×25% = 50% + 10% = 60%,

[0078] The above calculations show that when the target open-circuit voltage of the energy storage battery is 3.24V, its corresponding SOC is 60%. This process utilizes the stable correspondence between voltage characteristic points and charge characteristic points, and uses the voltage position ratio to inversely deduce the charge ratio, thus achieving a refined estimation of SOC within the phase change platform region.

[0079] In one optional embodiment, the energy storage battery may further perform temperature correction and aging correction on the calculated target capacity value. Optionally, the temperature correction formula may include:

[0080]

[0081] T0 is the standard temperature value, T is the battery temperature, and kT is the material temperature coefficient. This is the temperature correction value.

[0082] Formulas for aging correction may include:

[0083]

[0084] SOH is the aging ratio value. As the attenuation factor, This is the aging correction value.

[0085] The energy storage system can output the final amount of energy stored in the battery using the following formula:

[0086]

[0087] Optionally, kT is used to indicate the correction ratio that needs to be supplemented for SOC when the temperature deviates from the standard temperature (usually 25°C) by 1°C. The larger its absolute value, the more sensitive the battery is to temperature. At different temperatures, kT has different value ranges. For example, in the normal temperature range (0 - 45°C), the value range of kT includes: (0.001, 0.003), and the preferred value can be 0.002; in the low temperature range (<0°C), the value range of kT can include: (0.003, 0.006), and the preferred value can be 0.004; in the high temperature range (>45°C), the value range of kT can include: (0.002, 0.004), and the preferred value can be 0.003, with the unit of % / °C. α is used to indicate "the correction ratio that needs to be supplemented for SOC when the voltage deviates by 1V due to battery aging". The larger its absolute value, the more serious the damage of aging to the "voltage-SOC correspondence". At different aging degrees, α has different value ranges. For example, for a new battery (SOH > 90%), the value range of α can include (0.3, 0.6), and the preferred value is 0.4; for a mid-term aging battery (70% < SOH ≤ 90%), the value range of α can include (0.6, 1.0), and the preferred value is 0.8; for an end-term aging battery (SOH ≤ 70%), the value range of α can include (1.0, 1.5), and the preferred value is 1.2, with the unit of % / V.

[0088] The following combines specific numerical examples to illustrate the calculation processes of temperature correction (ΔSOCT), aging correction (ΔSOCA), and the final battery charge (SOCfinal), continuing with the parameter settings of the lithium iron phosphate battery in the previous text: SOCbase: 60%, λ: 0.4, standard temperature T0: 25°C, current battery temperature T: 35°C, material temperature coefficient kT: 0.002% / °C, voltage deviation ΔV: 0.01V, aging ratio SOH: 80%, attenuation factor α: 0.5% / V.

[0089] Substitute the above values into the formula ΔSOCT = kT × (T - T0) × λ

[0090] ΔSOCT = 0.002% / (°C) × (35°C - 25°C) × 0.4 = 0.008%

[0091] Substitute the above values into the formula ΔSOCA = α× (1 - SOH) × ΔV. ΔSOCA = 0.5% / V × 0.2× 0.01V = 0.001%.

[0092] Substitute the above values into the formula SOCfinal = SOCbase + ΔSOCT + ΔSOCA. SOCfinal = 60% + 0.008% + 0.001% = 60.009%.

[0093] In this embodiment, the energy storage system improves the estimation accuracy and reliability of the State of Charge (SOC) of the energy storage battery through a multi-level collaborative mechanism. First, based on material-specific parameters, two-stage corrections—aging quadratic function compensation and temperature exponential compensation—eliminate the nonlinear interference of temperature and aging on the initial open-circuit voltage, yielding a target open-circuit voltage reflecting the true state. Then, relying on a voltage-charge mapping table constructed based on multi-order differential features, and utilizing the stable correspondence between voltage and charge characteristic points in the phase transition plateau region, fine-grained preliminary SOC positioning is achieved through interpolation between characteristic points. Finally, dynamic corrections for temperature and aging are superimposed to further compensate for minor errors. This overall solution effectively solves the problems of environmental and aging interference and error accumulation inherent in traditional methods, significantly improving the accuracy and stability of energy storage system state monitoring and providing a reliable basis for battery management.

[0094] In one alternative embodiment, the energy storage battery first constructs and stores a voltage-capacity mapping table before estimating the battery's state of charge (SOC). (See also...) Figure 2 The steps for constructing a voltage-to-electricity mapping table may include:

[0095] Step 201: In response to the charging test of the energy storage battery under test, acquire the open circuit voltage data and capacity data of the energy storage battery under test during the charging test.

[0096] In one optional embodiment, open-circuit voltage data is often subject to noise interference. After acquiring the data, the energy storage system can first filter the open-circuit voltage data based on the current fluctuation characteristics of the energy storage battery under test. Specifically, the current variance of the energy storage battery under test is first calculated. When the current variance is less than a first threshold, the energy storage system can filter the voltage data based on a first window value. When the current variance is not less than the first threshold, the energy storage system can filter the voltage data based on a second window value. The first window value is smaller than the second window value.

[0097] It's understandable that the current fluctuates during the charging and discharging of energy storage batteries (e.g., a sudden current surge during acceleration in a new energy vehicle, or current changes during power adjustment in an energy storage station). Large current fluctuations indicate strong environmental interference and high signal noise; small current fluctuations indicate a relatively "clean" signal with weaker noise interference. When the current variance is less than the first threshold (small fluctuations, relatively clean signal), the energy storage system uses a small window filter (first window value) to preserve high-frequency voltage characteristics (e.g., subtle changes in dV / dQ in the phase transition plateau region), allowing the differential signal to accurately reflect the true voltage changes and ensuring the integrity of the signal in the phase transition plateau region. When the current variance is greater than or equal to the first threshold (large fluctuations, high noise), the energy storage system uses a large window filter (second window value) to suppress interference, stabilize the differential signal, and prevent distortion of the differential result due to large current fluctuations and noise superposition.

[0098] Step 202: Determine the phase transition plateau region based on the first-order differential characteristics of the open-circuit voltage data and the electrical quantity data.

[0099] During charging testing, the energy storage system calculates the first derivative of the ratio of open-circuit voltage data to energy value data in real time. When the first derivative value first falls below a second threshold, it is marked as the start of the phase transition plateau region; when the first derivative value rises back above the second threshold, it is marked as the end of the phase transition plateau region. (Refer to...) Figure 3 When the charge value is 20%, it can be regarded as the starting point of the phase change plateau region, and when the charge value is 70%, it can be regarded as the ending point of the phase change plateau region. The corresponding open circuit voltage is located in the range of 3.3V to 3.4V. The open circuit voltage in the phase change plateau region changes relatively slowly with the increase of charge value.

[0100] Step 203: Within the phase change platform region, determine multiple voltage feature points and the corresponding electrical feature points based on the second-order differential characteristics of the open-circuit voltage data and electrical quantity value data.

[0101] The energy storage system calculates the second derivative of the ratio of open-circuit voltage data to energy value data and generates a corresponding second derivative curve. The horizontal axis of the second derivative curve represents the energy value, and the vertical axis represents the second derivative value. The negative abrupt change points of the second derivative curve can be used to determine voltage and energy characteristic points.

[0102] Optionally, the energy storage system can scan the second derivative curve. When the second derivative values ​​of N consecutive sampling points are all below a third threshold, the open-circuit voltage and energy value corresponding to the sampling point with the smallest second derivative value are determined as a set of corresponding voltage and energy characteristic points, respectively. To avoid noise interference, the energy storage system performs dual judgment on energy characteristic points through threshold screening and continuous verification. Only when N consecutive sampling points are all below the third threshold can they be judged as valid voltage characteristic points.

[0103] When multiple voltage characteristic points exist, the energy storage system can sort the determined voltage characteristic points from smallest to largest according to the second derivative value, and then label the first three voltage characteristic points after sorting as the main peak characteristic point, the secondary peak characteristic point, and the tertiary peak characteristic point, respectively. The main peak characteristic point is the voltage characteristic point with the most drastic change trend.

[0104] In this embodiment, the construction process of the voltage-to-charge mapping table ensures the reliability and accuracy of the mapping relationship through precise feature analysis and range limitation. The overall process relies on the judgment rules of first-order differential features and second-order differential features to construct a mapping table that accurately reflects the relationship between open-circuit voltage and charge, providing a high-quality benchmark mapping relationship for subsequent SOC estimation and improving the accuracy of battery state monitoring from the source.

[0105] Figure 4Flowchart of another energy storage battery charge state determination device provided in the embodiments of this application Figure 3 .like Figure 4 As shown, the method may include:

[0106] Step 401: Construct a voltage and energy characteristic table.

[0107] The energy storage system collects the charge value data and open-circuit voltage data of the energy storage battery under test during the charging process, and then constructs a voltage and charge characteristic table. The specific process may include:

[0108] Step 4011: Dynamic adaptive filtering based on current fluctuations.

[0109] The appropriate window value is dynamically selected based on the severity of current fluctuations to adaptively filter the open-circuit voltage data.

[0110] Step 4012, dual-path differential feature localization.

[0111] First, the phase transition plateau region is determined using a first-order differential feature, and then multiple negative abrupt change points are determined using a second-order differential feature. When N consecutive sampling points are detected to be below a third threshold, the corresponding voltage feature point is determined. For example, N can be set to 3, and the third threshold can be set to -0.8 V / (Ah)². When three consecutive sampling points are less than -0.8 V / (Ah)², a voltage feature point can be confirmed. When multiple voltage feature points exist, the voltage feature point with the smallest sampling point can be identified as the main peak feature point.

[0112] Step 4013, multi-peak correlation topology network correction.

[0113] As energy storage batteries age, their internal structure may change (e.g., electrode material loss, electrolyte degradation, separator aging), leading to a decrease in the stability of voltage characteristic points. For example, the position of voltage characteristic points may shift (e.g., a peak value of 3.6V in a new battery may become 3.5V after aging). Furthermore, the signal of voltage characteristic points may weaken and the boundaries may become blurred (e.g., a previously sharp peak becomes flat, making it difficult to distinguish the start and end points). These issues affect the accuracy of energy storage battery SOC estimation. To address this, energy storage systems can use multi-peak correlation topology networks to correct voltage characteristic points.

[0114] Specifically, multi-peak correlation topology networks can indicate the relative correlation patterns between different voltage characteristic points (such as relative position, distance, and the proportion of change trends). For example, voltage characteristic point A is 2.8V, voltage characteristic point B is 3.2V, and voltage characteristic point C is 3.6V. The difference between B and A, and C is 0.4V. When voltage characteristic point A becomes blurred due to battery aging, making it difficult to distinguish whether it is 2.6V or 2.8V, the specific value of voltage characteristic point A can be determined by the positional relationship between A and B.

[0115] Step 402: Determine the target voltage value.

[0116] The initial voltage value is collected and corrected based on battery temperature and battery aging level to obtain the corresponding target voltage value.

[0117] Step 403: Determine the target power value using dual-path interpolation.

[0118] The electrical quantity value is interpolated based on the positional relationship between the target voltage value and different voltage feature points to obtain the corresponding target electrical quantity value.

[0119] Step 404, Dynamic Error Compensation.

[0120] Based on the location of the target voltage value, different coefficients are dynamically selected to perform dual compensation for temperature and aging.

[0121] The above process can effectively suppress noise interference, accurately extract voltage feature points, and correct the offset caused by aging through multi-peak correlation. Combined with the dynamic compensation mechanism, it significantly improves the accuracy and reliability of SOC estimation of energy storage batteries under different aging levels and complex operating conditions.

[0122] In one optional embodiment, the battery's temperature response characteristics exhibit significant range specificity: in the low-temperature region (e.g., T < 0°C), the ion migration activation energy increases sharply, and the voltage shift exhibits strong nonlinearity (exponential relationship with temperature); in the normal-temperature region (0°C ≤ T ≤ 45°C), the reaction kinetics are relatively stable, and the voltage shift is approximately linear; in the high-temperature region (T > 45°C), electrolyte decomposition and side reactions intensify, and the voltage shift is suppressed (exhibiting logarithmic slowdown). The energy storage system can be pre-configured with temperature compensation coefficients associated with multiple temperature ranges. When performing temperature compensation for the initial open-circuit voltage, a matching temperature compensation coefficient can be dynamically selected based on the battery temperature.

[0123] Specifically, the energy storage system can set the temperature compensation coefficients for the low-temperature zone, normal-temperature zone, and high-temperature zone as k1, k2, and k3, respectively. When the battery temperature is at the boundary between different zones (e.g., within ±2℃), the energy storage system can use a weighted average algorithm to fuse the compensation values ​​of adjacent zones (the weights transition linearly with temperature) to avoid abrupt changes in the compensation values. For example, when the battery temperature is 0℃, k1 accounts for 40% and k2 accounts for 60%, and the temperature compensation formula can be set as follows:

[0124]

[0125] For example, when the battery temperature is 40℃, k2 accounts for 50% and k3 accounts for 50%, the temperature compensation formula can be set as follows:

[0126]

[0127] Optionally, the energy storage system can also be configured with different compensation models to suit the characteristics of the energy storage battery in different temperature ranges.

[0128] In the low-temperature region, the temperature compensation model can be set as follows:

[0129]

[0130] n is the low-temperature adjustment factor, which can be 3-8, to adjust the compensation sensitivity at low temperatures.

[0131] In the high-temperature region, the temperature compensation model can be set as follows:

[0132]

[0133] Using a logarithmic suppression model can avoid overcompensation caused by side reactions.

[0134] Optionally, the value range of k1 may include (0.03, 0.05), with a preferred value of 0.04; the value range of k2 may include (0.01, 0.02), with a preferred value of 0.015; the value range of k3 may include (0.005, 0.01), with a preferred value of 0.008; and the value range of n may include (3, 8), with a preferred value of 5.

[0135] The above calculation process is described below based on the preferred values ​​and specific values ​​of each coefficient. The standard temperature T0 = 25℃, the first voltage compensation value ΔVcomp_base = 0.1V, and n = 5.

[0136] In the low-temperature region (battery temperature -10℃), k1=0.04, ΔVcomp_final=0.04 exp(-7)×0.1≈0.04×0.00091×0.1≈3.64× V.

[0137] At room temperature (battery temperature 30℃), k2=0.015, ΔVcomp_final=0.015⋅(30-25)×0.1=7.5× V.

[0138] In the high-temperature region (battery temperature 50℃), k3=0.008, ΔVcomp_final=0.008 log(∣50-25∣+1) 0.1 = 0.008 log(26×0.1≈0.008×1.415×0.1≈1.13× V.

[0139] In this embodiment, the multi-temperature range dynamic compensation strategy precisely adapts to the voltage offset characteristics at different temperatures by configuring an exponential enhancement model for the low-temperature range and a logarithmic suppression model for the high-temperature range. Combined with the weighted average transition at the boundary of the range to eliminate abrupt changes in the compensation value, it not only enhances the adaptability to different battery materials and scenarios, but also significantly improves the voltage compensation accuracy across the entire temperature range, reduces SOC estimation error, and enhances the management reliability of the energy storage system.

[0140] Corresponding to the above embodiments, this application also provides an energy storage system, including: an electronic device and a computer-readable storage medium, to perform the above-described energy storage battery charge state determination method. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 500 may include a processor 501, a memory 502, and a communication unit 503. These components communicate through one or more buses. Those skilled in the art will understand that the structure of the electronic device shown in the figure does not constitute a limitation on the embodiment of this application. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0141] The communication unit 503 is used to establish a communication channel, enabling the electronic device to communicate with other devices. It receives user data from other devices or sends user data to other devices.

[0142] The processor 501 serves as the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes software programs, instructions, and / or modules stored in the memory 502, and calls data stored in the memory to perform various functions and / or process data. The processor may be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 501 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.

[0143] The memory 502 is used to store the execution instructions of the processor 501. The memory 502 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0144] When the execution instructions in memory 502 are executed by processor 501, the electronic device 500 is able to perform some or all of the steps in the above embodiments.

[0145] In a specific implementation, this application also provides a computer storage medium, wherein the computer storage medium may store a program, which, when executed, may include some or all of the steps of the various embodiments of the energy storage battery charge state determination method provided in this application. The storage medium may be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0146] In a specific implementation, this application also provides a computer program product, wherein the computer program product includes executable instructions, which, when executed on a computer, cause the computer to perform some or all of the steps in various embodiments of the energy storage battery charge state determination method provided in this application.

[0147] This application also provides a non-transitory computer-readable storage medium that stores computer instructions, which cause the computer to execute the energy storage battery charge state determination method provided in this application.

[0148] The aforementioned non-transitory computer-readable storage medium may be any combination of one or more computer-readable media. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in connection with an instruction execution system, apparatus, or device.

[0149] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0150] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0151] Those skilled in the art will clearly understand that the techniques in the embodiments of this application can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application or some parts of the embodiments.

[0152] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

Claims

1. A method for determining the state of charge of an energy storage battery, characterized in that, include: Obtain the initial open-circuit voltage of the energy storage battery under test; The initial open-circuit voltage is corrected based on the aging degree and temperature of the energy storage battery under test to obtain the target open-circuit voltage; A voltage-to-capacity mapping table is obtained. The voltage-to-capacity mapping table is constructed based on the multi-order differential characteristics between the open-circuit voltage and the capacity value of the energy storage battery under test. The voltage-to-capacity mapping table contains the mapping relationship between multiple voltage feature points and capacity feature points. The target charge value corresponding to the target open circuit voltage is determined based on the positional relationship between the target open circuit voltage and the voltage feature point; The step of correcting the initial open-circuit voltage based on the aging degree and temperature of the energy storage battery under test to obtain the target open-circuit voltage includes: Obtain the aging compensation coefficient and temperature compensation coefficient corresponding to the material properties of the energy storage battery under test, and obtain the aging ratio value and battery temperature of the energy storage battery under test. The first voltage compensation value is determined based on the aging ratio value and the aging compensation coefficient. The second voltage compensation value is determined based on the battery temperature, the temperature compensation coefficient, and the first voltage compensation value. The target open-circuit voltage is determined based on the second voltage compensation value and the initial open-circuit voltage; The formula for calculating the first voltage compensation value includes: , The first voltage compensation value is given by a and b, which are aging compensation coefficients, and SOH is the aging ratio value. The calculation formula for the second voltage compensation value includes: , Here, k is the second voltage compensation value, k is the temperature compensation coefficient, and T is the battery temperature. Standard temperature value.

2. The method according to claim 1, characterized in that, Before obtaining the voltage-to-electricity mapping table, the method further includes: In response to the charging test of the energy storage battery under test, the open circuit voltage data and capacity data of the energy storage battery under test are acquired during the charging test. The phase transition plateau region is determined based on the first-order differential characteristics of the open-circuit voltage data and the electrical quantity value data; Within the phase transition platform region, multiple voltage feature points and corresponding electrical feature points are determined based on the second-order differential characteristics of the open-circuit voltage data and the electrical quantity value data.

3. The method according to claim 2, characterized in that, The determination of the phase transition plateau region based on the first-order differential characteristics of the open-circuit voltage data and the electrical quantity data includes: During the charging test, the first derivative of the ratio of the open-circuit voltage data to the power value data is calculated in real time. When the value of the first derivative first falls below the second threshold, it is marked as the starting point of the phase transition plateau region. When the value of the first derivative rises back above the second threshold, it is marked as the ending point of the phase transition plateau region.

4. The method according to claim 2, characterized in that, The determination of multiple voltage feature points and the corresponding electrical feature point for each voltage feature point based on the second-order differential features of the open-circuit voltage data and the electrical quantity value data includes: Calculate the second derivative of the ratio of the open-circuit voltage data to the electrical quantity data, and generate the corresponding second derivative curve, where the horizontal axis of the second derivative curve is the electrical quantity value and the vertical axis is the second derivative value. The voltage characteristic point and the electrical characteristic point are determined based on the negative abrupt change point of the second derivative curve.

5. The method according to claim 4, characterized in that, The determination of the voltage characteristic point and the charge characteristic point based on the negative abrupt change point of the second derivative curve includes: Scan the second derivative curve, and when the second derivative values ​​of N consecutive sampling points are all lower than the third threshold, the open-circuit voltage and charge value corresponding to the sampling point with the smallest second derivative value are determined as a set of corresponding voltage feature points and charge feature points.

6. The method according to claim 5, characterized in that, The method further includes: The identified voltage characteristic points are sorted in ascending order of their second derivative values, and the first three voltage characteristic points after sorting are successively labeled as the main peak characteristic point, the secondary peak characteristic point, and the tertiary peak characteristic point.

7. The method according to claim 2, characterized in that, Before determining the phase transition plateau region based on the first-order differential characteristics of the open-circuit voltage data and the electrical quantity data, the method further includes: The open-circuit voltage data is filtered based on the current fluctuation characteristics of the energy storage battery under test.

8. The method according to claim 7, characterized in that, The filtering process for the open-circuit voltage data based on the current fluctuation characteristics of the energy storage battery under test includes: Calculate the current variance of the energy storage battery under test; In response to the current variance value being less than a first threshold, the voltage data is filtered based on a first window value; in response to the current variance value being not less than the first threshold, the voltage data is filtered based on a second window value. Wherein, the first window value is smaller than the second window value.

9. The method according to claim 1, characterized in that, Determining the target charge value corresponding to the target open-circuit voltage based on the positional relationship between the target open-circuit voltage and the voltage feature point includes: Calculate the voltage difference between any two consecutive voltage characteristic points, and calculate the energy difference between the corresponding two consecutive energy characteristic points; Based on the voltage difference and the charge difference, determine the mapping relationship between open-circuit voltage and charge value between any two consecutive voltage characteristic points; Two target voltage feature points are identified where the target open-circuit voltage is located, and the target electrical quantity value corresponding to the target open-circuit voltage is determined based on the target mapping relationship between the open-circuit voltage and the electrical quantity value between the two target voltage feature points.

10. The method according to claim 1, characterized in that, The method further includes: Temperature correction and aging correction are performed on the target charge value based on the positional relationship between the target open-circuit voltage and the voltage characteristic point.

11. An energy storage system, characterized in that, include: Electronic devices and computer-readable storage media; The electronic device is configured to store computer program instructions in a memory and to execute the program instructions in a processor, wherein when the computer program instructions are executed by the processor, the electronic device performs the method according to any one of claims 1 to 10. The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 10.

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